动态避障新方法，让无人机更安全地避开移动障碍物
Obstacle Avoidance of UAV in Dynamic Environments Using Direction and Velocity-Adaptive Artificial Potential Field
- 用方向和相对速度调节斥力场，自适应优化避障策略
- 仿真中彻底解决局部极小问题，避障更平滑可靠
- 适合复杂动态环境下的无人机自主飞行系统
传统人工势场法（APF）存在局部极小值问题，且无法考虑运动障碍物的动力学特性。本文针对无人机（UAV）在动态密集空域中的自主避障难题，提出一种新型方向与相对速度加权人工势场（APF）。该方法引入有界权重函数 $ω(θ, v_{e})$，根据障碍物相对于无人机的方向和速度动态调整斥力势能。此鲁棒的APF形式被集成至模型预测控制（MPC）框架中，生成满足运动约束的无碰撞轨迹。仿真结果表明，该方法有效解决局部极小值问题，显著提升安全性，实现平滑、前瞻性的避障动作。系统保持优异路径完整性与可靠性能，验证了其在复杂环境中实现自主导航的可行性。
原文摘要 · Abstract (English)
The conventional Artificial Potential Field (APF) is fundamentally limited by the local minima issue and its inability to account for the kinematics of moving obstacles. This paper addresses the critical challenge of autonomous collision avoidance for Unmanned Aerial Vehicles (UAVs) operating in dynamic and cluttered airspace by proposing a novel Direction and Relative Velocity Weighted Artificial Potential Field (APF). In this approach, a bounded weighting function, $ω(θ,v_{e})$, is introduced to dynamically scale the repulsive potential based on the direction and velocity of the obstacle relative to the UAV. This robust APF formulation is integrated within a Model Predictive Control (MPC) framework to generate collision-free trajectories while adhering to kinematic constraints. Simulation results demonstrate that the proposed method effectively resolves local minima and significantly enhances safety by enabling smooth, predictive avoidance maneuvers. The system ensures superior path integrity and reliable performance, confirming its viability for autonomous navigation in complex environments.
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